HES 505 Fall 2022: Session 14
Matt Williamson
By the end of today, you should be able to:
Define spatial analysis
Describe the steps in planning a spatial analysis
Understand the structure of relational databases
Begin building a database for spatial analysis
“The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge. Spatial analysis extracts or creates new information from spatial data”.
The process of turning maps into information
Any- or everything we do with GIS
The use of computational and statistical algorithms to understand the relations between things that co-occur in space.
Describe and visualize locations or events
Quantify patterns
Characterize ‘suitability’
Determine (statistical) relations
Locational Fallacy: Error due to the spatial characterization chosen for elements of study
Atomic Fallacy: Applying conclusions from individuals to entire spatial units
Ecological Fallacy: Applying conclusions from aggregated information to individuals
Spatial analysis is an inherently complex endeavor and one that is advancing rapidly. So-called “best practices” for addressing many of these issues are still being developed and debated. This doesn’t mean you shouldn’t do spatial analysis, but you should keep these things in mind as you design, implement, and interpret your analyses
Acquisition (not really a focus, but see Resources)
Geoprocessing
Analysis
Visualization
Manipulation of data for subsequent use
Alignment (projections, cropping)
Data cleaning and transformation (measures, transformers)
Combination of multiple datasets (overlays, raster maths)
Selection and subsetting (predicates, measures)